Virtual Support for Real-World Movement:Using Chatbots to Overcome Barriers to Physical Activity

Conversational agents (CAs, aka chatbots) for behavioral interventions have great potential to improve patient engagement and provide solutions that can benefit human health. In this study, we examined the potential efficacy of chatbots in assisting with the resolution of specific barriers that people frequently encounter when doing behavioral interventions for the purpose of increasing physical activity (PA). To do this, six common barriers (i.e., things that stand in the way of increasing PA) were targeted (e.g., stress and fatigue), we adopted domain knowledge (i.e., psychological theories and behavioral change techniques) to design six interventions aimed at tackling each of these six barriers. These interventions were then incorporated into consultative conversations, which were subsequently integrated into a chatbot. A user study was conducted on non-clinical samples (n=77) where all participants were presented with three randomly but equally distributed chatbot interventions and a control condition. Each intervention conversation addressed a specific barrier to PA, while the control conversation did not address any barrier. The outcome variables were beliefs in PA engagement, attitudes toward the effectiveness of each intervention to resolve the barrier, and the overall chatbot experience. The results showed a significant increase in beliefs of PA engagement in most intervention groups compared to the control group, and positive attitudes toward the effectiveness of the interventions in reducing their respective barriers to PA, and positive chatbot experience. The results demonstrate that theory-grounded interventions delivered by chatbots can effectively help people overcome specific barriers to PA, thereby increasing their beliefs in PA engagement. These promising findings indicate that chatbot interventions can be an accessible and widely applicable solution for a larger population to promote PA.</p

Fetal macrosomia related to maternal poorly controlled type 1 diabetes strongly impairs serum lipoprotein concentrations and composition

Aims—To determine the effects of fetal macrosomia related to maternal type 1 diabetes on the lipid transport system. Methods—Serum lipoprotein concentrations and composition and lecithin:cholesterol acyltransferase (LCAT) activity were investigated in macrosomic newborns (mean birth weight, 4650 g; SEM, 90) and their mothers with poorly controlled type 1 diabetes, in appropriate for gestational age newborns (mean birth weight, 3616 g; SEM, 68) and their mothers with well controlled type 1 diabetes, and macrosomic (mean birth weight, 4555 g; SEM, 86) or appropriate for gestational age (mean birth weight, 3290 g; SEM, 45) newborns and their healthy mothers. Results—In mothers with well controlled type 1 diabetes, serum lipids, apolipoproteins, and lipoproteins were comparable with those of healthy mothers. Similarly, in their infants, these parameters did not differ from those of appropriate for gestational age newborns. Serum triglyceride, very low density lipoprotein (VLDL), apolipoprotein B100 (apo B100), and high density lipoprotein (HDL) triglyceride concentrations were higher, whereas serum apo A-I and HDL(3) concentrations were lower in mothers with diabetes and poor glycaemic control than in healthy mothers. Their macrosomic newborns had higher concentrations in all serum lipids and lipoproteins, with high apo A-I and apo B100 values compared with appropriate for gestational age newborns. In macrosomic infants of healthy mothers, there were no significant differences in lipoprotein profiles compared with those of appropriate for gestational age infants. LCAT activity was similar in both groups of mothers and newborns. Conclusion—Poorly controlled maternal type 1 diabetes and fetal macrosomia were associated with lipoprotein abnormalities. Macrosomic lipoprotein profiles related to poor metabolic control of type 1 diabetes appear to have implications for later metabolic diseases. Key Words: apolipoproteins • lipids • lipoproteins • lecithin:cholesterol acyltransferase • fetal macrosomia • maternal type 1 diabete